{"id":25253,"date":"2024-01-19T16:54:50","date_gmt":"2024-01-19T08:54:50","guid":{"rendered":"http:\/\/www.biocloudservice.com\/wordpress\/?p=25253"},"modified":"2024-01-19T16:54:52","modified_gmt":"2024-01-19T08:54:52","slug":"%e4%b8%a4%e5%88%86%e9%92%9f%e6%95%99%e4%bc%9a%e4%bd%a0%e4%bd%bf%e7%94%a8%e9%ab%98%e7%ba%a7%e7%bb%9f%e8%ae%a1%e5%88%86%e6%9e%90%e7%9a%84%e5%bc%ba%e5%a4%a7%e5%b7%a5%e5%85%b7r%e8%af%ad%e8%a8%80%e5%8c%85m","status":"publish","type":"post","link":"http:\/\/www.biocloudservice.com\/wordpress\/?p=25253","title":{"rendered":"\u4e24\u5206\u949f\u6559\u4f1a\u4f60\u4f7f\u7528\u9ad8\u7ea7\u7edf\u8ba1\u5206\u6790\u7684\u5f3a\u5927\u5de5\u5177R\u8bed\u8a00\u5305mnormt"},"content":{"rendered":"

R\u8bed\u8a00\u4f5c\u4e3a\u6570\u636e\u5206\u6790\u548c\u7edf\u8ba1\u5efa\u6a21\u7684\u6d41\u884c\u5de5\u5177\uff0c\u63d0\u4f9b\u4e86\u8bb8\u591a\u529f\u80fd\u5f3a\u5927\u7684\u6269\u5c55\u5305\u3002\u5176\u4e2d\uff0cmnormt\u5305\u662f\u4e00\u4e2a\u5e7f\u6cdb\u5e94\u7528\u4e8e\u9ad8\u7ea7\u7edf\u8ba1\u5206\u6790\u7684\u91cd\u8981\u5de5\u5177\u3002\u8be5\u5305\u4e3a\u7528\u6237\u63d0\u4f9b\u4e86\u5904\u7406\u591a\u5143\u6b63\u6001\u5206\u5e03\u548c\u591a\u5143t\u5206\u5e03\u7684\u529f\u80fd\uff0c\u4f7f\u5f97\u5728\u591a\u5143\u6570\u636e\u5206\u6790\u548c\u7edf\u8ba1\u5efa\u6a21\u4e2d\u66f4\u52a0\u65b9\u4fbf\u548c\u9ad8\u6548\u3002<\/p>\n

mnormt\u5305\u7684\u529f\u80fd\u6db5\u76d6\u4e86\u591a\u4e2a\u65b9\u9762\uff0c\u5305\u62ec\u591a\u5143\u6b63\u6001\u5206\u5e03\u7684\u968f\u673a\u62bd\u6837\u3001\u5bc6\u5ea6\u4f30\u8ba1\u3001\u5206\u5e03\u51fd\u6570\u8ba1\u7b97\u4ee5\u53ca\u5206\u4f4d\u6570\u8ba1\u7b97\u7b49\u3002\u6b64\u5916\uff0c\u8be5\u5305\u8fd8\u63d0\u4f9b\u4e86\u591a\u5143t\u5206\u5e03\u76f8\u5173\u7684\u529f\u80fd\uff0c\u5982\u968f\u673a\u62bd\u6837\u3001\u5bc6\u5ea6\u4f30\u8ba1\u3001\u5206\u5e03\u51fd\u6570\u8ba1\u7b97\u548c\u5206\u4f4d\u6570\u8ba1\u7b97\u7b49\u3002\u8fd9\u4e9b\u529f\u80fd\u7684\u7ed3\u5408\u4f7f\u5f97\u7528\u6237\u53ef\u4ee5\u66f4\u52a0\u7075\u6d3b\u5730\u8fdb\u884c\u591a\u5143\u6570\u636e\u7684\u5206\u6790\uff0c\u5e76\u80fd\u591f\u5229\u7528\u591a\u5143\u6b63\u6001\u5206\u5e03\u548c\u591a\u5143t\u5206\u5e03\u8fdb\u884c\u5047\u8bbe\u68c0\u9a8c\u3001\u53c2\u6570\u4f30\u8ba1\u548c\u7f6e\u4fe1\u533a\u95f4\u6784\u5efa\u7b49\u7edf\u8ba1\u63a8\u65ad\u3002<\/p>\n

\u5728\u4f7f\u7528mnormt\u5305\u8fdb\u884c\u5206\u6790\u65f6\uff0c\u7528\u6237\u53ef\u4ee5\u901a\u8fc7\u7b80\u5355\u7684\u51fd\u6570\u8c03\u7528\u5b8c\u6210\u590d\u6742\u7684\u7edf\u8ba1\u8ba1\u7b97\u3002\u4f8b\u5982\uff0c\u4f7f\u7528\u51fd\u6570dmnorm()\u53ef\u4ee5\u8ba1\u7b97\u591a\u5143\u6b63\u6001\u5206\u5e03\u7684\u5bc6\u5ea6\u51fd\u6570\u503c\uff0c\u800c\u51fd\u6570rmnorm()\u5219\u53ef\u4ee5\u751f\u6210\u591a\u5143\u6b63\u6001\u5206\u5e03\u7684\u968f\u673a\u6837\u672c\u3002\u7c7b\u4f3c\u5730\uff0c\u5bf9\u4e8e\u591a\u5143t\u5206\u5e03\uff0c\u51fd\u6570dt()\u53ef\u4ee5\u8ba1\u7b97\u5bc6\u5ea6\u51fd\u6570\u503c\uff0c\u51fd\u6570rt()\u53ef\u4ee5\u751f\u6210\u968f\u673a\u6837\u672c\u3002\u8fd9\u4e9b\u51fd\u6570\u7684\u53c2\u6570\u8bbe\u7f6e\u7075\u6d3b\uff0c\u5e76\u4e14\u63d0\u4f9b\u4e86\u4e30\u5bcc\u7684\u9009\u9879\uff0c\u4ee5\u6ee1\u8db3\u4e0d\u540c\u5206\u6790\u9700\u6c42\u3002<\/p>\n

\u6b64\u5916\uff0cmnormt\u5305\u8fd8\u63d0\u4f9b\u4e86\u4e00\u4e9b\u7528\u4e8e\u591a\u5143\u6570\u636e\u53ef\u89c6\u5316\u548c\u6a21\u578b\u62df\u5408\u7684\u529f\u80fd\u3002\u4f8b\u5982\uff0c\u51fd\u6570persp()\u53ef\u4ee5\u7ed8\u5236\u591a\u5143\u6b63\u6001\u5206\u5e03\u7684\u5bc6\u5ea6\u51fd\u6570\u4e09\u7ef4\u56fe\u50cf\uff0c\u51fd\u6570pairs.mvt()\u53ef\u4ee5\u7ed8\u5236\u591a\u5143t\u5206\u5e03\u7684\u6563\u70b9\u56fe\u77e9\u9635\u3002\u8fd9\u4e9b\u529f\u80fd\u6709\u52a9\u4e8e\u7528\u6237\u66f4\u597d\u5730\u7406\u89e3\u591a\u5143\u6570\u636e\u7684\u7279\u5f81\u548c\u7ed3\u6784\uff0c\u5e76\u8f85\u52a9\u6a21\u578b\u5efa\u7acb\u548c\u8bc4\u4f30\u8fc7\u7a0b\u3002<\/p>\n

\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0cmnormt\u5305\u5e7f\u6cdb\u5e94\u7528\u4e8e\u5404\u4e2a\u9886\u57df\u7684\u6570\u636e\u5206\u6790\u548c\u7edf\u8ba1\u5efa\u6a21\u4e2d\u3002\u4f8b\u5982\uff0c\u5728\u91d1\u878d\u9886\u57df\uff0c\u7814\u7a76\u4eba\u5458\u53ef\u4ee5\u5229\u7528mnormt\u5305\u4e2d\u7684\u51fd\u6570\u8fdb\u884c\u591a\u5143\u6295\u8d44\u7ec4\u5408\u7684\u98ce\u9669\u8bc4\u4f30\u548c\u4f18\u5316\uff1b\u5728\u751f\u7269\u533b\u5b66\u9886\u57df\uff0c\u7814\u7a76\u4eba\u5458\u53ef\u4ee5\u4f7f\u7528\u8be5\u5305\u8fdb\u884c\u591a\u5143\u751f\u7269\u6307\u6807\u7684\u5173\u8054\u5206\u6790\u548c\u6a21\u5f0f\u8bc6\u522b\u3002\u6b64\u5916\uff0cmnormt\u5305\u8fd8\u5728\u793e\u4f1a\u79d1\u5b66\u3001\u73af\u5883\u79d1\u5b66\u548c\u5de5\u7a0b\u9886\u57df\u7b49\u591a\u4e2a\u9886\u57df\u5f97\u5230\u4e86\u5e7f\u6cdb\u5e94\u7528\u3002<\/p>\n

\u8981\u4f7f\u7528mnormt\u5305\uff0c\u53ef\u4ee5\u5728R\u4e2d\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u8fdb\u884c\u5b89\u88c5\u548c\u52a0\u8f7d\uff1a<\/p>\n

> install.packages(“mnormt”) #\u5b89\u88c5mnormt\u8bed\u8a00\u5305<\/p>\n

> library(mnormt) #\u52a0\u8f7d\u8bed\u8a00\u5305<\/p>\n

\u793a\u4f8b\uff1a<\/p>\n

\u57fa\u56e0\u8868\u8fbe\u6570\u636e\u7684\u5dee\u5f02\u5206\u6790\u3002<\/p>\n

\u5047\u8bbe\u6211\u4eec\u6709\u4e24\u4e2a\u5b9e\u9a8c\u7ec4\uff0c\u6bcf\u4e2a\u7ec4\u4e2d\u5305\u542b\u591a\u4e2a\u6837\u672c\uff0c\u6211\u4eec\u5bf9\u8fd9\u4e9b\u6837\u672c\u8fdb\u884c\u4e86\u57fa\u56e0\u8868\u8fbe\u6c34\u5e73\u7684\u6d4b\u91cf\u3002\u73b0\u5728\u6211\u4eec\u60f3\u8981\u786e\u5b9a\u54ea\u4e9b\u57fa\u56e0\u5728\u8fd9\u4e24\u4e2a\u7ec4\u4e4b\u95f4\u5b58\u5728\u663e\u8457\u5dee\u5f02\u3002<\/p>\n

\u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u52a0\u8f7dmnormt\u5305\u548c\u4e00\u4e9b\u5176\u4ed6\u5fc5\u8981\u7684\u5305\uff0c\u7136\u540e\u8bfb\u53d6\u548c\u51c6\u5907\u6211\u4eec\u7684\u6570\u636e\u3002\u5047\u8bbe\u6211\u4eec\u7684\u6570\u636e\u662f\u4e00\u4e2a\u77e9\u9635\uff0c\u5176\u4e2d\u6bcf\u4e00\u884c\u4ee3\u8868\u4e00\u4e2a\u57fa\u56e0\uff0c\u6bcf\u4e00\u5217\u4ee3\u8868\u4e00\u4e2a\u6837\u672c\u3002<\/p>\n

# \u52a0\u8f7d\u6240\u9700\u7684\u5305<\/p>\n

> library(mnormt)<\/p>\n

> library(edgeR) # \u7528\u4e8e\u5dee\u5f02\u8868\u8fbe\u5206\u6790\u7684\u53e6\u4e00\u4e2a\u5e38\u7528\u5305<\/p>\n

# \u8bfb\u53d6\u548c\u51c6\u5907\u6570\u636e<\/p>\n

> data <- read.csv(“gene_expression_data.csv”, header = TRUE)<\/p>\n

> gene_expression <- data[, -1] # \u53bb\u6389\u7b2c\u4e00\u5217\uff0c\u5373\u57fa\u56e0\u540d\u79f0\u5217<\/p>\n

\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528mnormt\u5305\u4e2d\u7684\u51fd\u6570\u6765\u6bd4\u8f83\u4e24\u4e2a\u7ec4\u4e4b\u95f4\u7684\u57fa\u56e0\u8868\u8fbe\u5dee\u5f02\u3002\u5e38\u89c1\u7684\u65b9\u6cd5\u4e4b\u4e00\u662f\u4f7f\u7528Hotelling’s T-squared\u68c0\u9a8c\uff0c\u5b83\u53ef\u4ee5\u5728\u591a\u5143\u6b63\u6001\u5206\u5e03\u5047\u8bbe\u4e0b\u68c0\u6d4b\u7ec4\u4e4b\u95f4\u7684\u5dee\u5f02\u3002<\/p>\n

# \u6267\u884cHotelling’s T-squared\u68c0\u9a8c<\/p>\n

> group1 <- gene_expression[1:10, ] # \u7b2c\u4e00\u4e2a\u5b9e\u9a8c\u7ec4\u7684\u6837\u672c<\/p>\n

> group2 <- gene_expression[11:20, ] # \u7b2c\u4e8c\u4e2a\u5b9e\u9a8c\u7ec4\u7684\u6837\u672c<\/p>\n

# \u8ba1\u7b97\u6bcf\u4e2a\u57fa\u56e0\u7684Hotelling’s T-squared\u7edf\u8ba1\u91cf\u548cp-value<\/p>\n

> result <- apply(gene_expression, 1, function(x) {<\/p>\n

t_stat <- hotelling.test(x ~ group, data = data.frame(x = x, group = c(rep(“Group1”, 10), rep(“Group2”, 10))))$statistic<\/p>\n

p_value <- 1 – pt(t_stat, df = ncol(group1) – 1, lower.tail = FALSE)<\/p>\n

c(t_stat, p_value)<\/p>\n

})<\/p>\n

# \u5c06\u7ed3\u679c\u4fdd\u5b58\u5230\u4e00\u4e2a\u6570\u636e\u6846\u4e2d<\/p>\n

> result_df <- data.frame(Gene = rownames(gene_expression), T_statistic = result[, 1], P_value = result[, 2])<\/p>\n

# \u6253\u5370\u524d\u51e0\u884c\u7ed3\u679c<\/p>\n

> print(head(result_df))<\/p>\n

\u4e0a\u8ff0\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u9009\u62e9\u4e86\u524d10\u4e2a\u57fa\u56e0\u4f5c\u4e3a\u7b2c\u4e00\u4e2a\u5b9e\u9a8c\u7ec4\u7684\u6837\u672c\uff0c\u63a5\u4e0b\u6765\u768410\u4e2a\u57fa\u56e0\u4f5c\u4e3a\u7b2c\u4e8c\u4e2a\u5b9e\u9a8c\u7ec4\u7684\u6837\u672c\u3002\u7136\u540e\uff0c\u6211\u4eec\u4f7f\u7528hotelling.test()\u51fd\u6570\u6267\u884cHotelling’s T-squared\u68c0\u9a8c\uff0c\u5e76\u8ba1\u7b97\u6bcf\u4e2a\u57fa\u56e0\u7684\u7edf\u8ba1\u91cf\u548cp-value\u3002\u6700\u540e\uff0c\u6211\u4eec\u5c06\u7ed3\u679c\u4fdd\u5b58\u5728\u4e00\u4e2a\u6570\u636e\u6846\u4e2d\u5e76\u6253\u5370\u51fa\u524d\u51e0\u884c\u3002<\/p>\n

\"T-test-in-R-7\"<\/p>\n

\"Z1cqDF2\"<\/p>\n

\"zEtuQzg\"<\/p>\n

\u9664\u4e86Hotelling’s T-squared\u68c0\u9a8c\uff0cmnormt\u5305\u8fd8\u63d0\u4f9b\u4e86\u5176\u4ed6\u7528\u4e8e\u591a\u5143\u6b63\u6001\u5206\u5e03\u7684\u5047\u8bbe\u68c0\u9a8c\u65b9\u6cd5\uff0c\u5982Wilks’ lambda\u68c0\u9a8c\u548cPillai’s trace\u68c0\u9a8c\uff0c\u53ef\u4ee5\u6839\u636e\u5177\u4f53\u9700\u6c42\u9009\u62e9\u9002\u5f53\u7684\u65b9\u6cd5\u3002<\/p>\n

\"Size-power-curves-for-the-Wilks-Lambda-statistic-L-dashed-line-and-the-robust-Wilks\"<\/p>\n

\"585ix\"<\/p>\n

\u901a\u8fc7\u4f7f\u7528mnormt\u5305\uff0c\u6211\u4eec\u53ef\u4ee5\u65b9\u4fbf\u5730\u8fdb\u884c\u57fa\u56e0\u8868\u8fbe\u6570\u636e\u7684\u5dee\u5f02\u5206\u6790\uff0c\u5e76\u627e\u51fa\u5728\u4e0d\u540c\u5b9e\u9a8c\u7ec4\u4e4b\u95f4\u5177\u6709\u663e\u8457\u5dee\u5f02\u7684\u57fa\u56e0\u3002\u8fd9\u6709\u52a9\u4e8e\u6211\u4eec\u7406\u89e3\u57fa\u56e0\u5728\u4e0d\u540c\u751f\u7269\u6761\u4ef6\u4e0b\u7684\u8c03\u63a7\u548c\u529f\u80fd\uff0c\u5e76\u4e3a\u751f\u7269\u4fe1\u606f\u5b66\u7814\u7a76\u63d0\u4f9b\u91cd\u8981\u7684\u6d1e\u5bdf\u3002<\/p>\n

\u4ee5\u4e0a\u5c31\u662f\u5bf9R\u8bed\u8a00\u5305mnormt\u7684\u7b80\u5355\u4ecb\u7ecd\u5566\uff0cR\u8bed\u8a00\u5305mnormt\u662f\u4e00\u6b3e\u5f3a\u5927\u7684\u5de5\u5177\uff0c\u4e3a\u7528\u6237\u63d0\u4f9b\u4e86\u5904\u7406\u591a\u5143\u6b63\u6001\u5206\u5e03\u548c\u591a\u5143t\u5206\u5e03\u7684\u4e30\u5bcc\u529f\u80fd\u3002\u5176\u7b80\u6d01\u7684\u51fd\u6570\u63a5\u53e3\u548c\u7075\u6d3b\u7684\u53c2\u6570\u8bbe\u7f6e\u4f7f\u5f97\u7528\u6237\u80fd\u591f\u9ad8\u6548\u5730\u8fdb\u884c\u591a\u5143\u6570\u636e\u7684\u5206\u6790\u548c\u7edf\u8ba1\u63a8\u65ad\u3002\u65e0\u8bba\u662f\u5728\u5b66\u672f\u7814\u7a76\u8fd8\u662f\u5b9e\u9645\u5e94\u7528\u4e2d\uff0cmnormt\u5305\u90fd\u53d1\u6325\u7740\u91cd\u8981\u7684\u4f5c\u7528\uff0c\u4e3a\u9ad8\u7ea7\u7edf\u8ba1\u5206\u6790\u63d0\u4f9b\u4e86\u6709\u529b\u7684\u652f\u6301\u3002<\/p>\n

\u5c0f\u4f19\u4f34\u4eec\uff0c\u4eca\u5929\u6709\u6ca1\u6709\u5b66\u5230\u65b0\u77e5\u8bc6\u5462\uff0c\u60f3\u8981\u7ee7\u7eed\u4e86\u89e3R\u8bed\u8a00\u5185\u5bb9\u53ef\u4ee5\u6301\u7eed\u5173\u6ce8\u5c0f\u679c\u54e6\uff5e\uff5e\u6216\u8005\u4e5f\u53ef\u4ee5\u5173\u6ce8\u6211\u4eec\u7684\u5b98\u7f51\u4e5f\u4f1a\u6301\u7eed\u66f4\u65b0\u7684\u54e6\uff5e http:\/\/www.biocloudservice.com\/home.html<\/a><\/p>\n

References:<\/p>\n

    \n
  1. https:\/\/www.educba.com\/t-test-in-r\/<\/a><\/li>\n
  2. https:\/\/www.datanovia.com\/en\/blog\/how-to-perform-paired-pairwise-t-tests-in-r\/<\/a><\/li>\n
  3. https:\/\/www.datanovia.com\/en\/blog\/how-to-perform-multiple-t-test-in-r-for-different-variables\/<\/a><\/li>\n
  4. https:\/\/www.researchgate.net\/figure\/Size-power-curves-for-the-Wilks-Lambda-statistic-L-dashed-line-and-the-robust-Wilks_fig2_46494016<\/a><\/li>\n
  5. https:\/\/stats.stackexchange.com\/questions\/557428\/ridge-trace-plot-interpretation<\/a><\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"

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